Ghazaleh Kazeminejad


2019

pdf bib
Improving Low-Resource Morphological Learning with Intermediate Forms from Finite State Transducers
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

2018

pdf bib
Automatically Extracting Qualia Relations for the Rich Event Ontology
Ghazaleh Kazeminejad | Claire Bonial | Susan Windisch Brown | Martha Palmer
Proceedings of the 27th International Conference on Computational Linguistics

Commonsense, real-world knowledge about the events that entities or “things in the world” are typically involved in, as well as part-whole relationships, is valuable for allowing computational systems to draw everyday inferences about the world. Here, we focus on automatically extracting information about (1) the events that typically bring about certain entities (origins), (2) the events that are the typical functions of entities, and (3) part-whole relationships in entities. These correspond to the agentive, telic and constitutive qualia central to the Generative Lexicon. We describe our motivations and methods for extracting these qualia relations from the Suggested Upper Merged Ontology (SUMO) and show that human annotators overwhelmingly find the information extracted to be reasonable. Because ontologies provide a way of structuring this information and making it accessible to agents and computational systems generally, efforts are underway to incorporate the extracted information to an ontology hub of Natural Language Processing semantic role labeling resources, the Rich Event Ontology.

pdf bib
A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68% (unambiguous verbs) and 92.90% (all verbs).

2017

pdf bib
Creating lexical resources for polysynthetic languages—the case of Arapaho
Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages